The NLP Architect requires Python 3.6+ running on a Linux* or UNIX-based OS (like Mac OS). We recommend using the library with Ubuntu 16.04+.
Before installing the library make sure you has the most recent packages listed below:
Ubuntu* 16.04+ or CentOS* 7.4+
Mac OS X*
Tool to install Python dependencies
Enables loading of hdf5 formats
Retrieves information about installed libraries
The default installation of NLP Architect use CPU-based binaries of all deep learning frameworks. Intel Optimized MKL-DNN binaries will be installed if a Linux is detected. GPU backed is supported online on Linux and if a GPU is present. See details below for instructions on how to install each backend.
venv are up to date before installing.
pip3 install -U pip setuptools venv
We recommend installing NLP Architect in a virtual environment to self-contain the work done using the library.
To create and activate a new virtual environment (or skip this step and use the wizard below):
python3.6 -m venv .nlp_architect_env source .nlp_architect_env/bin/activate
Select the desired configuration of your system:
|Install in developer mode?|
Run the following commands to install NLP Architect:
It is recommended to install NLP Architect in development mode to utilize all its features, examples and solutions.
Install from source¶
To get started, clone our repository:
git clone https://github.com/NervanaSystems/nlp-architect.git cd nlp-architect
Selecting a backend¶
NLP Architect supports CPU, GPU and Intel Optimized Tensorflow (MKL-DNN) backends. Users can select the desired backend using a dedicated environment variable (default: CPU). (MKL-DNN and GPU backends are supported only on Linux)
NLP Architect is installed using pip and it is recommended to install in development mode.
pip3 install .
pip3 install -e .
Once installed, the
nlp_architect command provides additional options to work with the library, issue
nlp_architect -h to see all options.
Compiling Intel® optimized Tensorflow with MKL-DNN¶
NLP Architect supports MKL-DNN flavor of Tensorflow out of the box, however, if the user wishes to compile Tensorflow we provide instructions below.
Tensorflow has a guide guide for compiling and installing Tensorflow with with MKL-DNN optimization. Make sure to install all required tools: bazel and python development dependencies.
Alternatively, follow the instructions below to compile and install the latest version of Tensorflow with MKL-DNN:
Clone Tensorflow repository from GitHub:
git clone https://github.com/tensorflow/tensorflow cd tensorflow
Configure Tensorflow for compilation:
Compile Tensorflow with MKL-DNN:
bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
Create pip package in
Install Tensorflow pip package:
pip install <tensorflow package name>.whl
Refer to this guide for specific configuration to get optimal performance when running your model.